Introduction: Breast cancer
Materials and methods
Materials
26 explanatory variables.
- Condition
- Tumor type
| patient_id | gender | education | treatment_data | id_healthcenter | id_treatment_region | hereditary_history | birth_date | age | weight | thickness_tumor | marital_status | marital_length | pregnency_experience | giving_birth | age_FirstGivingBirth | abortion | blood | taking_heartMedicine | taking_blood_pressure_medicine | taking_gallbladder_disease_medicine | smoking | alcohol | breast_pain | radiation_history | Birth_control(Contraception) | menstrual_age | menopausal_age | Benign_malignant_cancer | condition |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 111036008041 | 0 | 4 | 2019 | 1.11e+09 | 1.11e+09 | 1 | 1989 | 30 | 69 | 0.90 | 1 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 1 | death |
| 111035996130 | 0 | 6 | 2019 | 1.11e+09 | 1.11e+09 | 0 | 1989 | 30 | 71 | 0.80 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 2 | 0 | 0 | death |
| 111035971333 | 0 | 5 | 2019 | 1.11e+09 | 1.11e+09 | 0 | 1989 | 30 | 74 | 0.90 | 1 | 0 | 0 | 0 | 0 | 1 | 4 | 1 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 1 | death |
| 111036018485 | 0 | 5 | 2019 | 1.11e+09 | 1.11e+09 | 1 | 1989 | 30 | 75 | 0.70 | 1 | 1 | 1 | 3 | 1 | 0 | 2 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | death |
| 111035985474 | 0 | 1 | 2019 | 1.11e+09 | 1.11e+09 | 0 | 2009 | 10 | 70 | 0.25 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | death |
| 111035903616 | 0 | 3 | 2019 | 1.11e+09 | 1.11e+09 | 1 | 1989 | 30 | 79 | 0.70 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | death |
| 111036003507 | 0 | 4 | 2019 | 1.11e+09 | 1.11e+09 | 1 | 1990 | 29 | 96 | 0.10 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 1 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 2 | 0 | 1 | death |
| 111036026259 | 0 | 5 | 2019 | 1.11e+09 | 1.11e+09 | 0 | 1990 | 29 | 75 | 0.80 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 2 | 0 | 0 | death |
Process
Cleaning the data
Unrealistic age and weight proportions.
Cleaning the data
| Column names |
| Removed special characters from variable names |
| Variable values |
| Greedy cleanup of binary variables |
| Blood type had non-conforming entries which were set to NA |
| Birth dates of other than 4 numbers were set to NA |
| Filter out samples |
| Only include women > 20 years, as they are the primary risk group |
| Remove abnormal attribute combinations |
| Removing columns |
| Remove singular columns (with only one value for all samples) |
Augmenting the data
| Values changed |
| Categorical variables as factor |
| Explicit values for categorical variables |
| Adding columns |
| Age at treatment |
| Normalised numerical variables |
Exploratory Analysis
Distributions
Distributions
Distributions
Distributions
MCA
MCA rotation
Model
Predicting Condition
Reduced Model
- Education
- Age
- Weight
- Tumor type
- Hereditary history
- Giving birth
- Age FirstGivingBirth
- Radiation history
- Menstrual age
- Abortion
- Breast pain
| Model | Sensitivity | Specificity | Balanced accuracy |
|---|---|---|---|
| Max_pred | 88% | 42% | 65% |
| Red_pred | 90% | 48% | 69% |
| baseline | 100% | 0% | 50% |
| Note: | |||
| Positive class = Death |
Shiny app
Shiny App
Discussion & Conclusion
Discussion
- Greedy cleaning approach
- Disagreement between MCA and reduced model
- A general set of rules for valid entries
Conclusion
- Possible to predict both tumor type and outcome.
- The prediction accuracy.
- Shiny app
Bibliography
- 2020 Cancer incidence and mortality in EU-27 countries EU Science Hub
- WHO Guide to cancer early diagnosis World Health Organization. Cancer
- Breast Cancer Dataset Kaggle
- Breast cancer incidence (invasive) statistics (10 March 2020). Cancer Research UK
- Height Percentile Calculator for Men and Women in the United States DQYDJ
- Weight Gain After Breast Cancer Diagnosis May Be a Bigger Issue Than Thought in Australia (13 March 2020) Breastcancer.org
- Github Repository Group 9 Final Project
MCA
Predicting tumortype
Reduced Model
- Age (norm)
- Weight (norm)
- Hereditary history
- Smoking
- Radiation therapy
- Menstrual age
- Pregnancy experience
- Abortion
- Breast pain
| model | sensitivity | specificity | balanced_accuracy |
|---|---|---|---|
| Max_pred | 69% | 28% | 48% |
| Red_pred | 81% | 24% | 53% |
| baseline | 100% | 0% | 50% |
| Note: | |||
| Positive class = Malignant |